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    "ExecuteTime": {
     "end_time": "2023-12-05T01:39:49.075712400Z",
     "start_time": "2023-12-05T01:39:48.796503Z"
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   "outputs": [],
   "source": [
    "import plotly.express as px #导入Plotly.express工具，命名为px"
   ]
  },
  {
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   "source": [
    "data = dict( #准备漏斗数据\n",
    "    number=[59, 32, 18, 9, 2,77],\n",
    "    stage=[\"访问数\", \"下载数\", \"注册数\", \"搜索数\", \"付款数\",\"123\"])\n",
    "fig = px.funnel(data, x='number', y='stage') #把数据传进漏斗图\n",
    "fig.show() #显示漏斗图"
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   "metadata": {
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    "ExecuteTime": {
     "end_time": "2023-12-05T02:23:57.119621900Z",
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   ],
   "source": [
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "stages = ['访问数', '下载数', '注册数', '搜索数', '付款数']\n",
    "fm_number = [59, 32, 18, 9, 3]\n",
    "m_number = [50, 24, 20, 6, 5]\n",
    "colors = {'fm_color': 'rgb(155,155,127)', 'm_color': 'rgb(123,88,60)'}\n",
    "\n",
    "fe_male = pd.DataFrame({'number': fm_number, 'stage': stages})\n",
    "male = pd.DataFrame({'number': m_number, 'stage': stages})\n",
    "\n",
    "fe_male['sex'] = 'fm_color'\n",
    "male['sex'] = 'm_color'\n",
    "\n",
    "df = pd.concat([fe_male, male], axis=0)\n",
    "fig = px.funnel(df, x='number', y='stage', color='sex',color_discrete_map=colors)\n",
    "\n",
    "fig.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-05T02:51:40.615875Z",
     "start_time": "2023-12-05T02:51:40.588277500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "    点赞数     转发数  热度指数  文章评级     浏览量\n0  2646  1347.0     7     5  260004\n1   816   816.0     4     6  100004\n2  1224   612.0     6     5  164502\n3  1261  1261.0     6     6  163001\n4  1720  1720.0     7     5  260401",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>点赞数</th>\n      <th>转发数</th>\n      <th>热度指数</th>\n      <th>文章评级</th>\n      <th>浏览量</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2646</td>\n      <td>1347.0</td>\n      <td>7</td>\n      <td>5</td>\n      <td>260004</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>816</td>\n      <td>816.0</td>\n      <td>4</td>\n      <td>6</td>\n      <td>100004</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1224</td>\n      <td>612.0</td>\n      <td>6</td>\n      <td>5</td>\n      <td>164502</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1261</td>\n      <td>1261.0</td>\n      <td>6</td>\n      <td>6</td>\n      <td>163001</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1720</td>\n      <td>1720.0</td>\n      <td>7</td>\n      <td>5</td>\n      <td>260401</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "csv='../csv/易速鲜花微信软文.csv'\n",
    "df_ads = pd.read_csv(csv) # 读入数据\n",
    "df_ads.head() # 显示前几行数据"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-05T11:01:31.379167Z",
     "start_time": "2023-12-05T11:01:31.353988300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#导入数据可视化所需要的库\n",
    "import matplotlib.pyplot as plt # Matplotlib – Python画图工具库\n",
    "import seaborn as sns # Seaborn – 统计学数据可视化工具库\n",
    "plt.rcParams['font.sans-serif'] = ['SimSun']  # 将字体设置为SimSun或其他支持中文字符的字体\n",
    "plt.plot(df_ads['点赞数'],df_ads['浏览量'],'r.', label='Training data') # 用matplotlib.pyplot的plot方法显示散点图\n",
    "plt.xlabel('点赞数') # x轴Label\n",
    "plt.ylabel('浏览量') # y轴Label\n",
    "plt.legend() # 显示图例\n",
    "plt.show() # 显示绘图结果！"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-05T11:05:04.470390100Z",
     "start_time": "2023-12-05T11:05:04.353647300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "(-0.5, 9.5, 0.0, 800000.0)"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=pd.concat([df_ads['浏览量'],df_ads[ '热度指数']],axis=1)\n",
    "fig=sns.boxplot(x='热度指数',y='浏览量',data=data)\n",
    "fig.axis(ymin=0,ymax=800000)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-05T11:05:27.516247Z",
     "start_time": "2023-12-05T11:05:27.376099500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "点赞数      0\n转发数     37\n热度指数     0\n文章评级     0\n浏览量      0\ndtype: int64"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ads.isna().sum() # NaN出现的次数"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-05T11:12:46.423272100Z",
     "start_time": "2023-12-05T11:12:46.403236900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "df_ads = df_ads.dropna() # 把出现了NaN的数据行删掉"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-05T11:21:06.907500900Z",
     "start_time": "2023-12-05T11:21:06.904991100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "    点赞数     转发数  热度指数  文章评级\n0  2646  1347.0     7     5\n1   816   816.0     4     6\n2  1224   612.0     6     5\n3  1261  1261.0     6     6\n4  1720  1720.0     7     5",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>点赞数</th>\n      <th>转发数</th>\n      <th>热度指数</th>\n      <th>文章评级</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2646</td>\n      <td>1347.0</td>\n      <td>7</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>816</td>\n      <td>816.0</td>\n      <td>4</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1224</td>\n      <td>612.0</td>\n      <td>6</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1261</td>\n      <td>1261.0</td>\n      <td>6</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1720</td>\n      <td>1720.0</td>\n      <td>7</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_ads.drop(['浏览量'],axis=1) # 特征集，Drop掉 \"标签\" 相关字段\n",
    "X.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-05T11:23:23.141487700Z",
     "start_time": "2023-12-05T11:23:23.136752800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "0    260004\n1    100004\n2    164502\n3    163001\n4    260401\nName: 浏览量, dtype: int64"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df_ads.浏览量 # 标签集    留下预测标签\n",
    " # 显示前几行数据\n",
    "y.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-05T11:33:29.209526200Z",
     "start_time": "2023-12-05T11:33:29.204013300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [],
   "source": [
    "#将数据集进行80%（训练集）和20%（验证集）的分割\n",
    "from sklearn.model_selection import train_test_split #导入train_test_split工具\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
    "                                   test_size=0.2, random_state=0)"
   ],
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     "end_time": "2023-12-05T11:33:34.295693Z",
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    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "data": {
      "text/plain": "       点赞数     转发数  热度指数  文章评级\n1074  1709   970.0     6     5\n404   1440  1440.0     4     4\n1375  1434  1454.0     7     5\n1317   832   832.0     6     6\n145    923   876.0     5     5",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>点赞数</th>\n      <th>转发数</th>\n      <th>热度指数</th>\n      <th>文章评级</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1074</th>\n      <td>1709</td>\n      <td>970.0</td>\n      <td>6</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>404</th>\n      <td>1440</td>\n      <td>1440.0</td>\n      <td>4</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1375</th>\n      <td>1434</td>\n      <td>1454.0</td>\n      <td>7</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1317</th>\n      <td>832</td>\n      <td>832.0</td>\n      <td>6</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>145</th>\n      <td>923</td>\n      <td>876.0</td>\n      <td>5</td>\n      <td>5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-12-05T11:34:21.986413300Z",
     "start_time": "2023-12-05T11:34:21.966134600Z"
    }
   }
  }
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